Overview

Dataset statistics

 9th May10th May
Number of variables1515
Number of observations9088
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows00
Duplicate rows (%)0.0%0.0%
Total size in memory10.7 KiB10.4 KiB
Average record size in memory121.5 B121.5 B

Variable types

 9th May10th May
DateTime11
Numeric99
Categorical55

Alerts

9th May10th May
THD Voltage L1 has constant value "" THD Voltage L1 has constant value "" Constant
Avg Current is highly overall correlated with Total Active power and 6 other fieldsAvg Current is highly overall correlated with Total Active power and 3 other fieldsHigh Correlation
Total Active power is highly overall correlated with Avg Current and 6 other fieldsTotal Active power is highly overall correlated with Avg Current and 3 other fieldsHigh Correlation
Total Reactive power is highly overall correlated with Avg Current and 6 other fieldsTotal Reactive power is highly overall correlated with Avg Current and 3 other fieldsHigh Correlation
Total Apparent power is highly overall correlated with Avg Current and 6 other fieldsTotal Apparent power is highly overall correlated with Avg Current and 3 other fieldsHigh Correlation
Import MWh is highly overall correlated with Import MVAhImport MWh is highly overall correlated with Avg Voltage and 1 other fieldsHigh Correlation
Import MVAh is highly overall correlated with Import MWhImport MVAh is highly overall correlated with Avg Voltage and 1 other fieldsHigh Correlation
Avg PF is highly overall correlated with Avg Current and 6 other fieldsAvg PF is highly overall correlated with Avg Current and 3 other fieldsHigh Correlation
THD Current L1 is highly overall correlated with Avg Current and 6 other fieldsAlert not present in High Correlation
THD Current L2 is highly overall correlated with Avg Current and 6 other fieldsAlert not present in High Correlation
THD Current L3 is highly overall correlated with Avg Current and 6 other fieldsAlert not present in High Correlation
THD Current L1 is highly imbalanced (88.9%) Alert not present in Imbalance
THD Current L2 is highly imbalanced (88.9%) Alert not present in Imbalance
THD Current L3 is highly imbalanced (88.9%) Alert not present in Imbalance
Time has unique values Time has unique values Unique
Avg Voltage has unique values Avg Voltage has unique values Unique
Avg Current has 63 (70.0%) zeros Avg Current has 74 (84.1%) zeros Zeros
Total Active power has 63 (70.0%) zeros Total Active power has 74 (84.1%) zeros Zeros
Total Reactive power has 64 (71.1%) zeros Total Reactive power has 73 (83.0%) zeros Zeros
Total Apparent power has 63 (70.0%) zeros Total Apparent power has 74 (84.1%) zeros Zeros
Alert not present in THD Current L1 has constant value "" Constant
Alert not present in THD Current L2 has constant value "" Constant
Alert not present in THD Current L3 has constant value "" Constant
Alert not present in Avg Voltage is highly overall correlated with Import MWh and 1 other fieldsHigh Correlation

Reproduction

 9th May10th May
Analysis started2023-07-03 10:26:05.8948372023-07-03 10:26:18.197124
Analysis finished2023-07-03 10:26:13.8923282023-07-03 10:26:26.738672
Duration8 seconds8.54 seconds
Software versionydata-profiling vv4.2.0ydata-profiling vv4.2.0
Download configurationconfig.jsonconfig.json

Variables

Time
Date

 9th May10th May
Distinct9088
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Memory size852.0 B836.0 B
 9th May10th May
Minimum2023-07-03 00:07:002023-07-03 00:05:00
Maximum2023-07-03 23:35:002023-07-03 23:49:00
2023-07-03T15:56:30.690839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T15:56:30.816427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Avg Voltage
Real number (ℝ)

 9th May10th May
Distinct9088
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean12758.14712736.462
 9th May10th May
Minimum12223.8512191.45
Maximum13331.2513293.75
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size852.0 B836.0 B
2023-07-03T15:56:30.942207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 9th May10th May
Minimum12223.8512191.45
5-th percentile12315.65312333.58
Q112580.12512528.288
median12739.7512685.5
Q312916.4512965.675
95-th percentile13227.3413246.782
Maximum13331.2513293.75
Range1107.41102.3
Interquartile range (IQR)336.325437.3875

Descriptive statistics

 9th May10th May
Standard deviation259.06753293.32244
Coefficient of variation (CV)0.0203060470.023030136
Kurtosis-0.38616187-0.93157449
Mean12758.14712736.462
Median Absolute Deviation (MAD)171.75209.85
Skewness0.109982210.26952515
Sum1148233.31120808.6
Variance67115.98786038.057
MonotonicityNot monotonicNot monotonic
2023-07-03T15:56:31.099704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12826.75 1
 
1.1%
12813.75 1
 
1.1%
12990.1 1
 
1.1%
12714.35 1
 
1.1%
12707.25 1
 
1.1%
12399 1
 
1.1%
12231.35 1
 
1.1%
12469.6 1
 
1.1%
12506.75 1
 
1.1%
12414.25 1
 
1.1%
Other values (80) 80
88.9%
ValueCountFrequency (%)
12748.3 1
 
1.1%
12602.25 1
 
1.1%
13107.45 1
 
1.1%
12968 1
 
1.1%
12948.1 1
 
1.1%
12783.5 1
 
1.1%
12622.45 1
 
1.1%
12398.15 1
 
1.1%
12191.45 1
 
1.1%
12354.15 1
 
1.1%
Other values (78) 78
88.6%
ValueCountFrequency (%)
12223.85 1
1.1%
12231.35 1
1.1%
12276 1
1.1%
12284.45 1
1.1%
12305.55 1
1.1%
12328 1
1.1%
12389.85 1
1.1%
12399 1
1.1%
12406.2 1
1.1%
12414.25 1
1.1%
ValueCountFrequency (%)
12191.45 1
1.1%
12197.65 1
1.1%
12255.05 1
1.1%
12312.3 1
1.1%
12331.2 1
1.1%
12338 1
1.1%
12354.15 1
1.1%
12360.85 1
1.1%
12365.45 1
1.1%
12374.35 1
1.1%
ValueCountFrequency (%)
12191.45 1
1.1%
12197.65 1
1.1%
12255.05 1
1.1%
12312.3 1
1.1%
12331.2 1
1.1%
12338 1
1.1%
12354.15 1
1.1%
12360.85 1
1.1%
12365.45 1
1.1%
12374.35 1
1.1%
ValueCountFrequency (%)
12223.85 1
1.1%
12231.35 1
1.1%
12276 1
1.1%
12284.45 1
1.1%
12305.55 1
1.1%
12328 1
1.1%
12389.85 1
1.1%
12399 1
1.1%
12406.2 1
1.1%
12414.25 1
1.1%

Avg PF
Categorical

 9th May10th May
Distinct53
Distinct (%)5.6%3.4%
Missing00
Missing (%)0.0%0.0%
Memory size852.0 B836.0 B
0.0
63 
0.98
19 
0.99
 
4
0.97
 
3
1.0
 
1
0.0
73 
0.98
0.99
 
6

Length

 9th May10th May
Max length44
Median length33
Mean length3.28888893.1704545
Min length33

Characters and Unicode

 9th May10th May
Total characters296279
Distinct characters64
Distinct categories22 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 9th May10th May
Unique10 ?
Unique (%)1.1%0.0%

Sample

 9th May10th May
1st row0.00.98
2nd row0.00.0
3rd row0.00.0
4th row0.980.0
5th row0.990.0

Common Values

ValueCountFrequency (%)
0.0 63
70.0%
0.98 19
 
21.1%
0.99 4
 
4.4%
0.97 3
 
3.3%
1.0 1
 
1.1%
ValueCountFrequency (%)
0.0 73
83.0%
0.98 9
 
10.2%
0.99 6
 
6.8%

Length

2023-07-03T15:56:31.232962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

9th May

2023-07-03T15:56:31.333956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:31.429717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 63
70.0%
0.98 19
 
21.1%
0.99 4
 
4.4%
0.97 3
 
3.3%
1.0 1
 
1.1%
ValueCountFrequency (%)
0.0 73
83.0%
0.98 9
 
10.2%
0.99 6
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 153
51.7%
. 90
30.4%
9 30
 
10.1%
8 19
 
6.4%
7 3
 
1.0%
1 1
 
0.3%
ValueCountFrequency (%)
0 161
57.7%
. 88
31.5%
9 21
 
7.5%
8 9
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 206
69.6%
Other Punctuation 90
30.4%
ValueCountFrequency (%)
Decimal Number 191
68.5%
Other Punctuation 88
31.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 153
74.3%
9 30
 
14.6%
8 19
 
9.2%
7 3
 
1.5%
1 1
 
0.5%
ValueCountFrequency (%)
0 161
84.3%
9 21
 
11.0%
8 9
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 90
100.0%
ValueCountFrequency (%)
. 88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 296
100.0%
ValueCountFrequency (%)
Common 279
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 153
51.7%
. 90
30.4%
9 30
 
10.1%
8 19
 
6.4%
7 3
 
1.0%
1 1
 
0.3%
ValueCountFrequency (%)
0 161
57.7%
. 88
31.5%
9 21
 
7.5%
8 9
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 296
100.0%
ValueCountFrequency (%)
ASCII 279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 153
51.7%
. 90
30.4%
9 30
 
10.1%
8 19
 
6.4%
7 3
 
1.0%
1 1
 
0.3%
ValueCountFrequency (%)
0 161
57.7%
. 88
31.5%
9 21
 
7.5%
8 9
 
3.2%

Avg Current
Real number (ℝ)

 9th May10th May
Distinct2815
Distinct (%)31.1%17.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.81911110.84102273
 9th May10th May
Minimum00
Maximum11.698.2
Zeros6374
Zeros (%)70.0%84.1%
Negative00
Negative (%)0.0%0.0%
Memory size852.0 B836.0 B
2023-07-03T15:56:31.507785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 9th May10th May
Minimum00
5-th percentile00
Q100
median00
Q33.65750
95-th percentile7.3836.059
Maximum11.698.2
Range11.698.2
Interquartile range (IQR)3.65750

Descriptive statistics

 9th May10th May
Standard deviation3.04288922.067544
Coefficient of variation (CV)1.67273412.4583687
Kurtosis0.569712224.3459232
Mean1.81911110.84102273
Median Absolute Deviation (MAD)00
Skewness1.37045862.3576196
Sum163.7274.01
Variance9.25917454.274738
MonotonicityNot monotonicNot monotonic
2023-07-03T15:56:31.618090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 63
70.0%
5.92 1
 
1.1%
6.39 1
 
1.1%
6.47 1
 
1.1%
7.08 1
 
1.1%
3.69 1
 
1.1%
5.18 1
 
1.1%
5.85 1
 
1.1%
2.77 1
 
1.1%
7.59 1
 
1.1%
Other values (18) 18
 
20.0%
ValueCountFrequency (%)
0 74
84.1%
6.43 1
 
1.1%
7.4 1
 
1.1%
5.7 1
 
1.1%
3.26 1
 
1.1%
7.9 1
 
1.1%
3.55 1
 
1.1%
5.18 1
 
1.1%
3.2 1
 
1.1%
6.08 1
 
1.1%
Other values (5) 5
 
5.7%
ValueCountFrequency (%)
0 63
70.0%
0.84 1
 
1.1%
2.77 1
 
1.1%
3.5 1
 
1.1%
3.56 1
 
1.1%
3.69 1
 
1.1%
3.85 1
 
1.1%
4.85 1
 
1.1%
5.18 1
 
1.1%
5.49 1
 
1.1%
ValueCountFrequency (%)
0 74
84.1%
3.05 1
 
1.1%
3.2 1
 
1.1%
3.26 1
 
1.1%
3.4 1
 
1.1%
3.55 1
 
1.1%
4.64 1
 
1.1%
5.18 1
 
1.1%
5.7 1
 
1.1%
6.02 1
 
1.1%
ValueCountFrequency (%)
0 74
82.2%
3.05 1
 
1.1%
3.2 1
 
1.1%
3.26 1
 
1.1%
3.4 1
 
1.1%
3.55 1
 
1.1%
4.64 1
 
1.1%
5.18 1
 
1.1%
5.7 1
 
1.1%
6.02 1
 
1.1%
ValueCountFrequency (%)
0 63
71.6%
0.84 1
 
1.1%
2.77 1
 
1.1%
3.5 1
 
1.1%
3.56 1
 
1.1%
3.69 1
 
1.1%
3.85 1
 
1.1%
4.85 1
 
1.1%
5.18 1
 
1.1%
5.49 1
 
1.1%

Total Active power
Real number (ℝ)

 9th May10th May
Distinct2814
Distinct (%)31.1%15.9%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean6880031101.136
 9th May10th May
Minimum00
Maximum443700291600
Zeros6374
Zeros (%)70.0%84.1%
Negative00
Negative (%)0.0%0.0%
Memory size852.0 B836.0 B
2023-07-03T15:56:31.837448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 9th May10th May
Minimum00
5-th percentile00
Q100
median00
Q31309500
95-th percentile286695224370
Maximum443700291600
Range443700291600
Interquartile range (IQR)1309500

Descriptive statistics

 9th May10th May
Standard deviation114321.8676522.19
Coefficient of variation (CV)1.6616552.4604307
Kurtosis0.550558674.1064018
Mean6880031101.136
Median Absolute Deviation (MAD)00
Skewness1.35843452.3325374
Sum61920002736900
Variance1.3069489 × 10105.8556456 × 109
MonotonicityNot monotonicNot monotonic
2023-07-03T15:56:31.933058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 63
70.0%
217800 1
 
1.1%
225000 1
 
1.1%
235800 1
 
1.1%
260100 1
 
1.1%
135900 1
 
1.1%
195300 1
 
1.1%
211500 1
 
1.1%
90900 1
 
1.1%
338400 1
 
1.1%
Other values (18) 18
 
20.0%
ValueCountFrequency (%)
0 74
84.1%
100800 2
 
2.3%
239400 1
 
1.1%
269100 1
 
1.1%
200700 1
 
1.1%
291600 1
 
1.1%
219600 1
 
1.1%
183600 1
 
1.1%
114300 1
 
1.1%
225000 1
 
1.1%
Other values (4) 4
 
4.5%
ValueCountFrequency (%)
0 63
70.0%
76500 1
 
1.1%
90900 1
 
1.1%
114300 1
 
1.1%
116100 1
 
1.1%
135900 1
 
1.1%
195300 1
 
1.1%
201600 1
 
1.1%
211500 1
 
1.1%
215100 1
 
1.1%
ValueCountFrequency (%)
0 74
84.1%
100800 2
 
2.3%
114300 1
 
1.1%
118800 1
 
1.1%
160200 1
 
1.1%
183600 1
 
1.1%
200700 1
 
1.1%
219600 1
 
1.1%
223200 1
 
1.1%
225000 1
 
1.1%
ValueCountFrequency (%)
0 74
82.2%
100800 2
 
2.2%
114300 1
 
1.1%
118800 1
 
1.1%
160200 1
 
1.1%
183600 1
 
1.1%
200700 1
 
1.1%
219600 1
 
1.1%
223200 1
 
1.1%
225000 1
 
1.1%
ValueCountFrequency (%)
0 63
71.6%
76500 1
 
1.1%
90900 1
 
1.1%
114300 1
 
1.1%
116100 1
 
1.1%
135900 1
 
1.1%
195300 1
 
1.1%
201600 1
 
1.1%
211500 1
 
1.1%
215100 1
 
1.1%

Total Reactive power
Real number (ℝ)

 9th May10th May
Distinct2113
Distinct (%)23.3%14.8%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean135806075
 9th May10th May
Minimum00
Maximum10440063000
Zeros6473
Zeros (%)71.1%83.0%
Negative00
Negative (%)0.0%0.0%
Memory size852.0 B836.0 B
2023-07-03T15:56:32.027226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 9th May10th May
Minimum00
5-th percentile00
Q100
median00
Q3193500
95-th percentile6093042300
Maximum10440063000
Range10440063000
Interquartile range (IQR)193500

Descriptive statistics

 9th May10th May
Standard deviation24033.80214998.086
Coefficient of variation (CV)1.7697942.4688208
Kurtosis2.1608835.3642127
Mean135806075
Median Absolute Deviation (MAD)00
Skewness1.69628812.5102854
Sum1222200534600
Variance5.7762363 × 1082.2494259 × 108
MonotonicityNot monotonicNot monotonic
2023-07-03T15:56:32.136798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 64
71.1%
40500 3
 
3.3%
18000 2
 
2.2%
45000 2
 
2.2%
38700 2
 
2.2%
49500 2
 
2.2%
66600 1
 
1.1%
52200 1
 
1.1%
19800 1
 
1.1%
34200 1
 
1.1%
Other values (11) 11
 
12.2%
ValueCountFrequency (%)
0 73
83.0%
18000 2
 
2.3%
17100 2
 
2.3%
42300 2
 
2.3%
43200 1
 
1.1%
55800 1
 
1.1%
38700 1
 
1.1%
63000 1
 
1.1%
41400 1
 
1.1%
33300 1
 
1.1%
Other values (3) 3
 
3.4%
ValueCountFrequency (%)
0 64
71.1%
16200 1
 
1.1%
18000 2
 
2.2%
19800 1
 
1.1%
34200 1
 
1.1%
36000 1
 
1.1%
38700 2
 
2.2%
40500 3
 
3.3%
41400 1
 
1.1%
43200 1
 
1.1%
ValueCountFrequency (%)
0 73
83.0%
16200 1
 
1.1%
17100 2
 
2.3%
18000 2
 
2.3%
27000 1
 
1.1%
33300 1
 
1.1%
38700 1
 
1.1%
41400 1
 
1.1%
42300 2
 
2.3%
43200 1
 
1.1%
ValueCountFrequency (%)
0 73
81.1%
16200 1
 
1.1%
17100 2
 
2.2%
18000 2
 
2.2%
27000 1
 
1.1%
33300 1
 
1.1%
38700 1
 
1.1%
41400 1
 
1.1%
42300 2
 
2.2%
43200 1
 
1.1%
ValueCountFrequency (%)
0 64
72.7%
16200 1
 
1.1%
18000 2
 
2.3%
19800 1
 
1.1%
34200 1
 
1.1%
36000 1
 
1.1%
38700 2
 
2.3%
40500 3
 
3.4%
41400 1
 
1.1%
43200 1
 
1.1%

Total Apparent power
Real number (ℝ)

 9th May10th May
Distinct2615
Distinct (%)28.9%17.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean7092032829.545
 9th May10th May
Minimum00
Maximum461700304200
Zeros6374
Zeros (%)70.0%84.1%
Negative00
Negative (%)0.0%0.0%
Memory size852.0 B836.0 B
2023-07-03T15:56:32.232092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 9th May10th May
Minimum00
5-th percentile00
Q100
median00
Q31415250
95-th percentile300375233370
Maximum461700304200
Range461700304200
Interquartile range (IQR)1415250

Descriptive statistics

 9th May10th May
Standard deviation117965.3480142.121
Coefficient of variation (CV)1.66335792.4411584
Kurtosis0.671315453.8982163
Mean7092032829.545
Median Absolute Deviation (MAD)00
Skewness1.38229242.2915366
Sum63828002889000
Variance1.3915822 × 10106.4227596 × 109
MonotonicityNot monotonicNot monotonic
2023-07-03T15:56:32.335903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 63
70.0%
144900 2
 
2.2%
243900 2
 
2.2%
318600 1
 
1.1%
234000 1
 
1.1%
243000 1
 
1.1%
270900 1
 
1.1%
202500 1
 
1.1%
223200 1
 
1.1%
107100 1
 
1.1%
Other values (16) 16
 
17.8%
ValueCountFrequency (%)
0 74
84.1%
247500 1
 
1.1%
279000 1
 
1.1%
210600 1
 
1.1%
124200 1
 
1.1%
304200 1
 
1.1%
228600 1
 
1.1%
192600 1
 
1.1%
123300 1
 
1.1%
234000 1
 
1.1%
Other values (5) 5
 
5.7%
ValueCountFrequency (%)
0 63
70.0%
87300 1
 
1.1%
107100 1
 
1.1%
126900 1
 
1.1%
131400 1
 
1.1%
144900 2
 
2.2%
202500 1
 
1.1%
209700 1
 
1.1%
216000 1
 
1.1%
223200 1
 
1.1%
ValueCountFrequency (%)
0 74
84.1%
112500 1
 
1.1%
123300 1
 
1.1%
124200 1
 
1.1%
129600 1
 
1.1%
169200 1
 
1.1%
192600 1
 
1.1%
210600 1
 
1.1%
228600 1
 
1.1%
232200 1
 
1.1%
ValueCountFrequency (%)
0 74
82.2%
112500 1
 
1.1%
123300 1
 
1.1%
124200 1
 
1.1%
129600 1
 
1.1%
169200 1
 
1.1%
192600 1
 
1.1%
210600 1
 
1.1%
228600 1
 
1.1%
232200 1
 
1.1%
ValueCountFrequency (%)
0 63
71.6%
87300 1
 
1.1%
107100 1
 
1.1%
126900 1
 
1.1%
131400 1
 
1.1%
144900 2
 
2.3%
202500 1
 
1.1%
209700 1
 
1.1%
216000 1
 
1.1%
223200 1
 
1.1%

THD Voltage L1
Categorical

 9th May10th May
Distinct11
Distinct (%)1.1%1.1%
Missing00
Missing (%)0.0%0.0%
Memory size852.0 B836.0 B
0
90 
0
88 

Length

 9th May10th May
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 9th May10th May
Total characters9088
Distinct characters11
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 9th May10th May
Unique00 ?
Unique (%)0.0%0.0%

Sample

 9th May10th May
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 90
100.0%
ValueCountFrequency (%)
0 88
100.0%

Length

2023-07-03T15:56:32.420663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

9th May

2023-07-03T15:56:32.516157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:32.594525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 90
100.0%
ValueCountFrequency (%)
0 88
100.0%

Most occurring characters

ValueCountFrequency (%)
0 90
100.0%
ValueCountFrequency (%)
0 88
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 90
100.0%
ValueCountFrequency (%)
Decimal Number 88
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 90
100.0%
ValueCountFrequency (%)
0 88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90
100.0%
ValueCountFrequency (%)
Common 88
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 90
100.0%
ValueCountFrequency (%)
0 88
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%
ValueCountFrequency (%)
ASCII 88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 90
100.0%
ValueCountFrequency (%)
0 88
100.0%

THD Voltage L2
Real number (ℝ)

 9th May10th May
Distinct7872
Distinct (%)86.7%81.8%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.93311111.7057955
 9th May10th May
Minimum0.180.22
Maximum3.192.91
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size852.0 B836.0 B
2023-07-03T15:56:32.704396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 9th May10th May
Minimum0.180.22
5-th percentile0.7350.791
Q11.50751.3625
median2.051.785
Q32.4452.0925
95-th percentile2.7712.5855
Maximum3.192.91
Range3.012.69
Interquartile range (IQR)0.93750.73

Descriptive statistics

 9th May10th May
Standard deviation0.656366650.56935681
Coefficient of variation (CV)0.339539020.33377789
Kurtosis-0.0967961710.29171205
Mean1.93311111.7057955
Median Absolute Deviation (MAD)0.4250.35
Skewness-0.51838568-0.42115685
Sum173.98150.11
Variance0.430817180.32416718
MonotonicityNot monotonicNot monotonic
2023-07-03T15:56:32.830019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.06 3
 
3.3%
2.25 2
 
2.2%
2.45 2
 
2.2%
0.91 2
 
2.2%
1.57 2
 
2.2%
1.7 2
 
2.2%
2.43 2
 
2.2%
1.84 2
 
2.2%
2.46 2
 
2.2%
2.22 2
 
2.2%
Other values (68) 69
76.7%
ValueCountFrequency (%)
1.78 2
 
2.3%
2.01 2
 
2.3%
1.82 2
 
2.3%
1.51 2
 
2.3%
1.64 2
 
2.3%
1.87 2
 
2.3%
1.83 2
 
2.3%
2.09 2
 
2.3%
1.37 2
 
2.3%
2.1 2
 
2.3%
Other values (62) 68
77.3%
ValueCountFrequency (%)
0.18 1
1.1%
0.34 1
1.1%
0.48 1
1.1%
0.59 1
1.1%
0.6 1
1.1%
0.9 1
1.1%
0.91 2
2.2%
0.95 1
1.1%
1 1
1.1%
1.14 1
1.1%
ValueCountFrequency (%)
0.22 1
1.1%
0.23 1
1.1%
0.33 1
1.1%
0.38 1
1.1%
0.77 1
1.1%
0.83 1
1.1%
0.94 1
1.1%
0.96 1
1.1%
0.98 1
1.1%
0.99 1
1.1%
ValueCountFrequency (%)
0.22 1
1.1%
0.23 1
1.1%
0.33 1
1.1%
0.38 1
1.1%
0.77 1
1.1%
0.83 1
1.1%
0.94 1
1.1%
0.96 1
1.1%
0.98 1
1.1%
0.99 1
1.1%
ValueCountFrequency (%)
0.18 1
1.1%
0.34 1
1.1%
0.48 1
1.1%
0.59 1
1.1%
0.6 1
1.1%
0.9 1
1.1%
0.91 2
2.3%
0.95 1
1.1%
1 1
1.1%
1.14 1
1.1%

THD Voltage L3
Real number (ℝ)

 9th May10th May
Distinct6763
Distinct (%)74.4%71.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.63966671.5409091
 9th May10th May
Minimum0.520.1
Maximum2.982.25
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size852.0 B836.0 B
2023-07-03T15:56:32.972064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 9th May10th May
Minimum0.520.1
5-th percentile0.87250.8735
Q11.2751.335
median1.611.645
Q31.94251.77
95-th percentile2.4972.036
Maximum2.982.25
Range2.462.15
Interquartile range (IQR)0.66750.435

Descriptive statistics

 9th May10th May
Standard deviation0.4958920.39206977
Coefficient of variation (CV)0.302434640.25444056
Kurtosis-0.119113962.5441751
Mean1.63966671.5409091
Median Absolute Deviation (MAD)0.340.195
Skewness0.27049274-1.3208759
Sum147.57135.6
Variance0.245908880.1537187
MonotonicityNot monotonicNot monotonic
2023-07-03T15:56:33.097837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.29 4
 
4.4%
1.84 4
 
4.4%
1.91 3
 
3.3%
1.17 2
 
2.2%
1.22 2
 
2.2%
1.6 2
 
2.2%
1.53 2
 
2.2%
1.27 2
 
2.2%
1.98 2
 
2.2%
1.55 2
 
2.2%
Other values (57) 65
72.2%
ValueCountFrequency (%)
1.67 4
 
4.5%
1.72 4
 
4.5%
1.74 3
 
3.4%
1.77 3
 
3.4%
1.24 2
 
2.3%
2.01 2
 
2.3%
1.26 2
 
2.3%
1.78 2
 
2.3%
1.76 2
 
2.3%
1.58 2
 
2.3%
Other values (53) 62
70.5%
ValueCountFrequency (%)
0.52 1
1.1%
0.74 1
1.1%
0.78 1
1.1%
0.84 1
1.1%
0.85 1
1.1%
0.9 1
1.1%
0.91 1
1.1%
0.92 1
1.1%
1.01 1
1.1%
1.02 1
1.1%
ValueCountFrequency (%)
0.1 1
1.1%
0.31 1
1.1%
0.33 1
1.1%
0.8 1
1.1%
0.87 1
1.1%
0.88 1
1.1%
0.92 1
1.1%
0.98 2
2.3%
1.16 1
1.1%
1.19 1
1.1%
ValueCountFrequency (%)
0.1 1
1.1%
0.31 1
1.1%
0.33 1
1.1%
0.8 1
1.1%
0.87 1
1.1%
0.88 1
1.1%
0.92 1
1.1%
0.98 2
2.2%
1.16 1
1.1%
1.19 1
1.1%
ValueCountFrequency (%)
0.52 1
1.1%
0.74 1
1.1%
0.78 1
1.1%
0.84 1
1.1%
0.85 1
1.1%
0.9 1
1.1%
0.91 1
1.1%
0.92 1
1.1%
1.01 1
1.1%
1.02 1
1.1%

THD Current L1
Categorical

 9th May10th May
Distinct31
Distinct (%)3.3%1.1%
Missing00
Missing (%)0.0%0.0%
Memory size852.0 B836.0 B
0.0
88 
15.95
 
1
11.7
 
1
0
88 

Length

 9th May10th May
Max length51
Median length31
Mean length3.03333331
Min length31

Characters and Unicode

 9th May10th May
Total characters27388
Distinct characters61
Distinct categories21 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 9th May10th May
Unique20 ?
Unique (%)2.2%0.0%

Sample

 9th May10th May
1st row0.00
2nd row0.00
3rd row0.00
4th row0.00
5th row0.00

Common Values

ValueCountFrequency (%)
0.0 88
97.8%
15.95 1
 
1.1%
11.7 1
 
1.1%
ValueCountFrequency (%)
0 88
100.0%

Length

2023-07-03T15:56:33.207448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

9th May

2023-07-03T15:56:33.291621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:33.380943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 88
97.8%
15.95 1
 
1.1%
11.7 1
 
1.1%
ValueCountFrequency (%)
0 88
100.0%

Most occurring characters

ValueCountFrequency (%)
0 176
64.5%
. 90
33.0%
1 3
 
1.1%
5 2
 
0.7%
9 1
 
0.4%
7 1
 
0.4%
ValueCountFrequency (%)
0 88
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183
67.0%
Other Punctuation 90
33.0%
ValueCountFrequency (%)
Decimal Number 88
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
96.2%
1 3
 
1.6%
5 2
 
1.1%
9 1
 
0.5%
7 1
 
0.5%
ValueCountFrequency (%)
0 88
100.0%
Other Punctuation
ValueCountFrequency (%)
. 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 273
100.0%
ValueCountFrequency (%)
Common 88
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
64.5%
. 90
33.0%
1 3
 
1.1%
5 2
 
0.7%
9 1
 
0.4%
7 1
 
0.4%
ValueCountFrequency (%)
0 88
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 273
100.0%
ValueCountFrequency (%)
ASCII 88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
64.5%
. 90
33.0%
1 3
 
1.1%
5 2
 
0.7%
9 1
 
0.4%
7 1
 
0.4%
ValueCountFrequency (%)
0 88
100.0%

THD Current L2
Categorical

 9th May10th May
Distinct31
Distinct (%)3.3%1.1%
Missing00
Missing (%)0.0%0.0%
Memory size852.0 B836.0 B
0.0
88 
15.81
 
1
11.95
 
1
0
88 

Length

 9th May10th May
Max length51
Median length31
Mean length3.04444441
Min length31

Characters and Unicode

 9th May10th May
Total characters27488
Distinct characters61
Distinct categories21 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 9th May10th May
Unique20 ?
Unique (%)2.2%0.0%

Sample

 9th May10th May
1st row0.00
2nd row0.00
3rd row0.00
4th row0.00
5th row0.00

Common Values

ValueCountFrequency (%)
0.0 88
97.8%
15.81 1
 
1.1%
11.95 1
 
1.1%
ValueCountFrequency (%)
0 88
100.0%

Length

2023-07-03T15:56:33.443654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

9th May

2023-07-03T15:56:33.537394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:33.615801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 88
97.8%
15.81 1
 
1.1%
11.95 1
 
1.1%
ValueCountFrequency (%)
0 88
100.0%

Most occurring characters

ValueCountFrequency (%)
0 176
64.2%
. 90
32.8%
1 4
 
1.5%
5 2
 
0.7%
8 1
 
0.4%
9 1
 
0.4%
ValueCountFrequency (%)
0 88
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 184
67.2%
Other Punctuation 90
32.8%
ValueCountFrequency (%)
Decimal Number 88
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
95.7%
1 4
 
2.2%
5 2
 
1.1%
8 1
 
0.5%
9 1
 
0.5%
ValueCountFrequency (%)
0 88
100.0%
Other Punctuation
ValueCountFrequency (%)
. 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274
100.0%
ValueCountFrequency (%)
Common 88
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
64.2%
. 90
32.8%
1 4
 
1.5%
5 2
 
0.7%
8 1
 
0.4%
9 1
 
0.4%
ValueCountFrequency (%)
0 88
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274
100.0%
ValueCountFrequency (%)
ASCII 88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
64.2%
. 90
32.8%
1 4
 
1.5%
5 2
 
0.7%
8 1
 
0.4%
9 1
 
0.4%
ValueCountFrequency (%)
0 88
100.0%

THD Current L3
Categorical

 9th May10th May
Distinct31
Distinct (%)3.3%1.1%
Missing00
Missing (%)0.0%0.0%
Memory size852.0 B836.0 B
0.0
88 
14.34
 
1
11.54
 
1
0
88 

Length

 9th May10th May
Max length51
Median length31
Mean length3.04444441
Min length31

Characters and Unicode

 9th May10th May
Total characters27488
Distinct characters61
Distinct categories21 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 9th May10th May
Unique20 ?
Unique (%)2.2%0.0%

Sample

 9th May10th May
1st row0.00
2nd row0.00
3rd row0.00
4th row0.00
5th row0.00

Common Values

ValueCountFrequency (%)
0.0 88
97.8%
14.34 1
 
1.1%
11.54 1
 
1.1%
ValueCountFrequency (%)
0 88
100.0%

Length

2023-07-03T15:56:33.694007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

9th May

2023-07-03T15:56:33.789272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:33.867579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 88
97.8%
14.34 1
 
1.1%
11.54 1
 
1.1%
ValueCountFrequency (%)
0 88
100.0%

Most occurring characters

ValueCountFrequency (%)
0 176
64.2%
. 90
32.8%
1 3
 
1.1%
4 3
 
1.1%
3 1
 
0.4%
5 1
 
0.4%
ValueCountFrequency (%)
0 88
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 184
67.2%
Other Punctuation 90
32.8%
ValueCountFrequency (%)
Decimal Number 88
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
95.7%
1 3
 
1.6%
4 3
 
1.6%
3 1
 
0.5%
5 1
 
0.5%
ValueCountFrequency (%)
0 88
100.0%
Other Punctuation
ValueCountFrequency (%)
. 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274
100.0%
ValueCountFrequency (%)
Common 88
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
64.2%
. 90
32.8%
1 3
 
1.1%
4 3
 
1.1%
3 1
 
0.4%
5 1
 
0.4%
ValueCountFrequency (%)
0 88
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274
100.0%
ValueCountFrequency (%)
ASCII 88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
64.2%
. 90
32.8%
1 3
 
1.1%
4 3
 
1.1%
3 1
 
0.4%
5 1
 
0.4%
ValueCountFrequency (%)
0 88
100.0%

Import MWh
Real number (ℝ)

 9th May10th May
Distinct5730
Distinct (%)63.3%34.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean110.53211112.26205
 9th May10th May
Minimum109.85111.79
Maximum111.79112.73
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size852.0 B836.0 B
2023-07-03T15:56:33.961381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 9th May10th May
Minimum109.85111.79
5-th percentile109.85111.79
Q1109.85111.79
median110.435112.295
Q3111.0125112.69
95-th percentile111.702112.69
Maximum111.79112.73
Range1.940.94
Interquartile range (IQR)1.16250.9

Descriptive statistics

 9th May10th May
Standard deviation0.664452960.40500932
Coefficient of variation (CV)0.00601140210.0036077137
Kurtosis-1.1510908-1.8428956
Mean110.53211112.26205
Median Absolute Deviation (MAD)0.5850.395
Skewness0.48426844-0.082628493
Sum9947.899879.06
Variance0.441497740.16403255
MonotonicityDecreasingDecreasing
2023-07-03T15:56:34.071794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109.85 29
32.2%
110.81 4
 
4.4%
111.79 3
 
3.3%
111.68 1
 
1.1%
110.3 1
 
1.1%
110.67 1
 
1.1%
110.63 1
 
1.1%
110.61 1
 
1.1%
110.59 1
 
1.1%
110.55 1
 
1.1%
Other values (47) 47
52.2%
ValueCountFrequency (%)
112.69 31
35.2%
111.79 28
31.8%
112.17 2
 
2.3%
112.2 1
 
1.1%
111.81 1
 
1.1%
111.85 1
 
1.1%
111.89 1
 
1.1%
111.9 1
 
1.1%
111.91 1
 
1.1%
111.93 1
 
1.1%
Other values (20) 20
22.7%
ValueCountFrequency (%)
109.85 29
32.2%
109.86 1
 
1.1%
109.9 1
 
1.1%
109.94 1
 
1.1%
109.99 1
 
1.1%
110.02 1
 
1.1%
110.06 1
 
1.1%
110.1 1
 
1.1%
110.13 1
 
1.1%
110.17 1
 
1.1%
ValueCountFrequency (%)
111.79 28
31.8%
111.81 1
 
1.1%
111.85 1
 
1.1%
111.89 1
 
1.1%
111.9 1
 
1.1%
111.91 1
 
1.1%
111.93 1
 
1.1%
111.97 1
 
1.1%
112 1
 
1.1%
112.04 1
 
1.1%
ValueCountFrequency (%)
111.79 28
31.1%
111.81 1
 
1.1%
111.85 1
 
1.1%
111.89 1
 
1.1%
111.9 1
 
1.1%
111.91 1
 
1.1%
111.93 1
 
1.1%
111.97 1
 
1.1%
112 1
 
1.1%
112.04 1
 
1.1%
ValueCountFrequency (%)
109.85 29
33.0%
109.86 1
 
1.1%
109.9 1
 
1.1%
109.94 1
 
1.1%
109.99 1
 
1.1%
110.02 1
 
1.1%
110.06 1
 
1.1%
110.1 1
 
1.1%
110.13 1
 
1.1%
110.17 1
 
1.1%

Import MVAh
Real number (ℝ)

 9th May10th May
Distinct5730
Distinct (%)63.3%34.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean112.68444114.44795
 9th May10th May
Minimum111.99113.96
Maximum113.96114.92
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size852.0 B836.0 B
2023-07-03T15:56:34.324429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

 9th May10th May
Minimum111.99113.96
5-th percentile111.99113.96
Q1111.99113.96
median112.59114.48
Q3113.18114.89
95-th percentile113.8775114.89
Maximum113.96114.92
Range1.970.96
Interquartile range (IQR)1.190.93

Descriptive statistics

 9th May10th May
Standard deviation0.67655790.41732084
Coefficient of variation (CV)0.00600400440.003646381
Kurtosis-1.1571022-1.8391374
Mean112.68444114.44795
Median Absolute Deviation (MAD)0.60.41
Skewness0.48225768-0.085293813
Sum10141.610071.42
Variance0.457730590.17415669
MonotonicityDecreasingDecreasing
2023-07-03T15:56:34.450462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111.99 29
32.2%
112.97 4
 
4.4%
113.96 3
 
3.3%
113.85 1
 
1.1%
112.45 1
 
1.1%
112.82 1
 
1.1%
112.78 1
 
1.1%
112.76 1
 
1.1%
112.74 1
 
1.1%
112.7 1
 
1.1%
Other values (47) 47
52.2%
ValueCountFrequency (%)
114.89 31
35.2%
113.96 28
31.8%
114.36 2
 
2.3%
114.38 1
 
1.1%
113.99 1
 
1.1%
114.03 1
 
1.1%
114.07 1
 
1.1%
114.08 1
 
1.1%
114.09 1
 
1.1%
114.11 1
 
1.1%
Other values (20) 20
22.7%
ValueCountFrequency (%)
111.99 29
32.2%
112 1
 
1.1%
112.04 1
 
1.1%
112.08 1
 
1.1%
112.13 1
 
1.1%
112.17 1
 
1.1%
112.2 1
 
1.1%
112.24 1
 
1.1%
112.27 1
 
1.1%
112.31 1
 
1.1%
ValueCountFrequency (%)
113.96 28
31.8%
113.99 1
 
1.1%
114.03 1
 
1.1%
114.07 1
 
1.1%
114.08 1
 
1.1%
114.09 1
 
1.1%
114.11 1
 
1.1%
114.15 1
 
1.1%
114.18 1
 
1.1%
114.22 1
 
1.1%
ValueCountFrequency (%)
113.96 28
31.1%
113.99 1
 
1.1%
114.03 1
 
1.1%
114.07 1
 
1.1%
114.08 1
 
1.1%
114.09 1
 
1.1%
114.11 1
 
1.1%
114.15 1
 
1.1%
114.18 1
 
1.1%
114.22 1
 
1.1%
ValueCountFrequency (%)
111.99 29
33.0%
112 1
 
1.1%
112.04 1
 
1.1%
112.08 1
 
1.1%
112.13 1
 
1.1%
112.17 1
 
1.1%
112.2 1
 
1.1%
112.24 1
 
1.1%
112.27 1
 
1.1%
112.31 1
 
1.1%

Interactions

9th May

2023-07-03T15:56:12.763238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:25.548002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:06.665666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:18.428407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:07.457546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:19.377953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.265600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:20.178264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.980550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:21.075149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:09.697360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:21.926540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.414492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:22.875299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:11.147236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:23.726655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:11.996088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:24.721068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:12.847410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:25.648107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:06.775375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:18.527687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:07.626660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:19.459282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.349268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:20.291685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:09.063548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:21.175384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:09.779742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:22.040436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.496310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:22.974643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:11.229939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:23.825158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:12.080989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:24.800353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:12.929546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:25.724690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:06.860807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:18.611169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:07.704045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:19.544097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.430358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:20.393946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:09.147018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:21.259915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:09.863639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:22.142483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.580486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:23.058611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:11.313160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:23.924389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:12.163768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:24.892651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:13.012667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:25.825116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:06.931908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:18.711527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:07.783963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:19.643301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.506582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:20.492201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:09.226363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:21.359891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:09.946623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:22.241644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.662046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:23.162491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:11.393801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:24.025192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:12.246713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:24.990797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:13.097179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:25.924451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:07.015526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:18.811307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:07.863372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:19.727760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.581155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:20.576879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:09.296677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:21.458833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.025394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:22.341816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.730306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:23.249282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:11.463644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:24.124464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:12.330156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:25.090947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:13.179518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:26.022824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:07.098075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:18.911624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:07.941406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:19.841295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.658181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:20.692038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:09.378245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:21.558851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.097233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:22.458076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.814460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:23.341658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:11.546370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:24.224736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:12.412502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:25.174060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:13.263704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:26.108105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:07.186062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:19.010756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.014289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:19.926117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.731023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:20.792329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:09.446795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:21.661456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.175732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:22.572731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.896786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:23.441932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:11.713351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:24.309017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:12.496887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:25.277368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:13.376828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:26.191806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:07.272357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:19.202237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.097886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:20.009784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.814645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:20.892207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:09.531022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:21.741893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.247082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:22.675530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.974700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:23.541005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:11.791813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:24.407345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:12.580767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:25.358342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:13.463088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:26.294259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:07.363108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:19.293518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.180582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:20.094547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:08.898763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:20.976423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:09.614179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:21.843060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:10.329706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:22.775775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:11.065594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:23.626644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:11.910832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:24.496848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

2023-07-03T15:56:12.678558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:25.457185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

9th May

2023-07-03T15:56:34.560475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

10th May

2023-07-03T15:56:34.701807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

9th May

Avg VoltageAvg CurrentTotal Active powerTotal Reactive powerTotal Apparent powerTHD Voltage L2THD Voltage L3Import MWhImport MVAhAvg PFTHD Current L1THD Current L2THD Current L3
Avg Voltage1.000-0.297-0.299-0.277-0.2930.000-0.202-0.355-0.3550.0460.0000.0000.000
Avg Current-0.2971.0000.9970.9551.0000.1420.1740.4190.4190.7470.9650.9650.965
Total Active power-0.2990.9971.0000.9580.9970.1430.1820.4280.4280.9410.7660.7660.766
Total Reactive power-0.2770.9550.9581.0000.9550.0940.1580.4300.4300.7630.8270.8270.827
Total Apparent power-0.2931.0000.9970.9551.0000.1460.1800.4220.4220.9200.7660.7660.766
THD Voltage L20.0000.1420.1430.0940.1461.0000.3550.2890.2890.0000.0000.0000.000
THD Voltage L3-0.2020.1740.1820.1580.1800.3551.0000.2230.2230.1100.0440.0440.044
Import MWh-0.3550.4190.4280.4300.4220.2890.2231.0001.0000.2810.1350.1350.135
Import MVAh-0.3550.4190.4280.4300.4220.2890.2231.0001.0000.2780.1350.1350.135
Avg PF0.0460.7470.9410.7630.9200.0000.1100.2810.2781.0000.5400.5400.540
THD Current L10.0000.9650.7660.8270.7660.0000.0440.1350.1350.5401.0001.0001.000
THD Current L20.0000.9650.7660.8270.7660.0000.0440.1350.1350.5401.0001.0001.000
THD Current L30.0000.9650.7660.8270.7660.0000.0440.1350.1350.5401.0001.0001.000

10th May

Avg VoltageAvg CurrentTotal Active powerTotal Reactive powerTotal Apparent powerTHD Voltage L2THD Voltage L3Import MWhImport MVAhAvg PF
Avg Voltage1.000-0.323-0.319-0.315-0.3190.1170.012-0.531-0.5310.149
Avg Current-0.3231.0001.0000.9661.000-0.014-0.230-0.011-0.0110.889
Total Active power-0.3191.0001.0000.9661.000-0.012-0.235-0.011-0.0110.922
Total Reactive power-0.3150.9660.9661.0000.966-0.032-0.197-0.001-0.0010.976
Total Apparent power-0.3191.0001.0000.9661.000-0.013-0.234-0.010-0.0100.922
THD Voltage L20.117-0.014-0.012-0.032-0.0131.000-0.128-0.115-0.1150.000
THD Voltage L30.012-0.230-0.235-0.197-0.234-0.1281.000-0.008-0.0080.325
Import MWh-0.531-0.011-0.011-0.001-0.010-0.115-0.0081.0001.0000.443
Import MVAh-0.531-0.011-0.011-0.001-0.010-0.115-0.0081.0001.0000.443
Avg PF0.1490.8890.9220.9760.9220.0000.3250.4430.4431.000

Missing values

9th May

2023-07-03T15:56:13.608910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.

10th May

2023-07-03T15:56:26.432034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.

9th May

2023-07-03T15:56:13.801260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

10th May

2023-07-03T15:56:26.649577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

9th May

TimeAvg VoltageAvg PFAvg CurrentTotal Active powerTotal Reactive powerTotal Apparent powerTHD Voltage L1THD Voltage L2THD Voltage L3THD Current L1THD Current L2THD Current L3Import MWhImport MVAh
023:3512826.750.000.0000.0002.061.170.00.00.0111.79113.96
123:2012822.550.000.0000.0001.621.750.00.00.0111.79113.96
223:0512730.950.000.0000.0001.471.610.00.00.0111.79113.96
322:5012628.450.985.9221780040500.022500002.220.780.00.00.0111.77113.94
422:3512527.050.993.5011610018000.013140002.202.280.00.00.0111.72113.90
522:2012568.900.983.8525650053100.014490002.041.910.00.00.0111.68113.85
622:0512276.000.000.0000.0001.781.290.00.00.0111.64113.81
721:5112577.900.000.0000.0001.772.100.00.00.0111.60113.78
821:3612586.800.000.0000.0002.732.150.00.00.0111.57113.74
921:2112560.350.000.0000.0002.522.290.00.00.0111.55113.72

10th May

TimeAvg VoltageAvg PFAvg CurrentTotal Active powerTotal Reactive powerTotal Apparent powerTHD Voltage L1THD Voltage L2THD Voltage L3THD Current L1THD Current L2THD Current L3Import MWhImport MVAh
023:4912748.300.986.432394004320024750001.780.87000112.73114.92
123:3412602.250.000.0000002.761.58000112.71114.90
223:1912576.100.000.0000002.111.67000112.69114.89
323:0412636.500.000.0000001.921.94000112.69114.89
422:4912527.350.000.0000001.271.39000112.69114.89
522:3412577.350.000.0000001.781.61000112.69114.89
622:1912528.600.000.0000001.252.05000112.69114.89
722:0412331.200.000.0000002.141.23000112.69114.89
821:4912509.500.000.0000002.912.25000112.69114.89
921:3412531.350.000.0000002.461.84000112.69114.89

9th May

TimeAvg VoltageAvg PFAvg CurrentTotal Active powerTotal Reactive powerTotal Apparent powerTHD Voltage L1THD Voltage L2THD Voltage L3THD Current L1THD Current L2THD Current L3Import MWhImport MVAh
8002:2213266.950.00.000.0002.100.520.00.00.0109.85111.99
8102:0712887.750.00.000.0002.452.320.00.00.0109.85111.99
8201:5213188.950.00.000.0002.062.110.00.00.0109.85111.99
8301:3713054.650.00.000.0002.781.170.00.00.0109.85111.99
8401:2213187.500.00.000.0002.251.860.00.00.0109.85111.99
8501:0713047.500.00.000.0002.291.540.00.00.0109.85111.99
8600:5213065.750.00.000.0001.951.120.00.00.0109.85111.99
8700:3713056.700.00.000.0002.221.600.00.00.0109.85111.99
8800:2213093.900.00.000.0001.700.920.00.00.0109.85111.99
8900:0713099.000.00.000.0001.681.450.00.00.0109.85111.99

10th May

TimeAvg VoltageAvg PFAvg CurrentTotal Active powerTotal Reactive powerTotal Apparent powerTHD Voltage L1THD Voltage L2THD Voltage L3THD Current L1THD Current L2THD Current L3Import MWhImport MVAh
7802:3513096.700.00.000002.051.99000111.79113.96
7902:2013100.250.00.000001.371.72000111.79113.96
8002:0513142.500.00.000001.971.62000111.79113.96
8101:5013102.850.00.000001.721.83000111.79113.96
8201:2013011.050.00.000002.611.21000111.79113.96
8301:0512859.700.00.000002.251.64000111.79113.96
8400:5012872.450.00.000002.401.56000111.79113.96
8500:3512850.700.00.000001.701.76000111.79113.96
8600:2012820.600.00.000001.831.67000111.79113.96
8700:0512898.500.00.000001.001.26000111.79113.96

Duplicate rows

9th May

TimeAvg VoltageAvg PFAvg CurrentTotal Active powerTotal Reactive powerTotal Apparent powerTHD Voltage L1THD Voltage L2THD Voltage L3THD Current L1THD Current L2THD Current L3Import MWhImport MVAh# duplicates
Dataset does not contain duplicate rows.

10th May

TimeAvg VoltageAvg PFAvg CurrentTotal Active powerTotal Reactive powerTotal Apparent powerTHD Voltage L1THD Voltage L2THD Voltage L3THD Current L1THD Current L2THD Current L3Import MWhImport MVAh# duplicates
Dataset does not contain duplicate rows.